Aidan Rocke AidanRocke

## conditional_dist.ipynb

      
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                AidanRocke
                / conditional_dist.ipynb
            
            
              Last active Feb 13, 2018
            
          
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## normal_approximation
"""
Created on Tue Mar 13 19:17:39 2018

@author: aidanrocke
"""

import tensorflow as tf
import numpy as np


## accumulated_gradients.ipynb

      
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                AidanRocke
                / accumulated_gradients.ipynb
            
            
              Last active May 9, 2018
            
              
                Minimal example of accumulated gradients in TensorFlow
              
          
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## google_translate_challenge.py
from googletrans import Translator
from itertools import product
from pandas import DataFrame, read_csv
import numpy as np
import string
import time
import os

# Get google translator object
translator = Translator()

## fixed_accumulated_gradients.ipynb

      
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                AidanRocke
                / fixed_accumulated_gradients.ipynb
            
            
              Created Aug 20, 2018
            
              
                Correct example of accumulated gradients
              
          
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## computing_gradients.tf
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 29 13:06:26 2018

@author: aidanrockea
"""
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp

## local_convex_test.jl


using LinearAlgebra
using Random

function local_convex_test(N)

	#srand(1234)

	sum = 0.0

## circuit_complexity.jl
using LinearAlgebra
using Random

function circuit_complexity(N)

	radii = zeros(1,N);

	for i=1:N


## crazy_paths.jl

using Random

function crazy_paths(N,delta_t)

	ddx, dx = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0)
	ddy, dy = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0)

	x, y = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0)

## monotone_approx.jl
using Distributions
using Statistics

function monotone_approx(N::Int64,n_trials::Int64,n::Int64)

	"""
		inputs:
			N: the range of U([-N,N])
			n_trials: the number of times we generate random vectors
			n: the 'dimension' of the random vector
	"""
	Created on Tue Mar 13 19:17:39 2018

	@author: aidanrocke
	"""

	import tensorflow as tf
	import numpy as np
	from googletrans import Translator
	from itertools import product
	from pandas import DataFrame, read_csv
	import numpy as np
	import string
	import time
	import os

	# Get google translator object
	translator = Translator()
	#!/usr/bin/env python3
	# -- coding: utf-8 --
	"""
	Created on Thu Nov 29 13:06:26 2018

	@author: aidanrockea
	"""
	import numpy as np
	import tensorflow as tf
	import tensorflow_probability as tfp


	using LinearAlgebra
	using Random

	function local_convex_test(N)

	#srand(1234)

	sum = 0.0
	using LinearAlgebra
	using Random

	function circuit_complexity(N)

	radii = zeros(1,N);

	for i=1:N

	using Random

	function crazy_paths(N,delta_t)

	ddx, dx = 10(2rand(N+1) .-1.0), 10(2rand(N+1) .-1.0)
	ddy, dy = 10(2rand(N+1) .-1.0), 10(2rand(N+1) .-1.0)

	x, y = 10(2rand(N+1) .-1.0), 10(2rand(N+1) .-1.0)
	using Distributions
	using Statistics

	function monotone_approx(N::Int64,n_trials::Int64,n::Int64)

	"""
	inputs:
	N: the range of U([-N,N])
	n_trials: the number of times we generate random vectors
	n: the 'dimension' of the random vector